Hybrid seed selection and seed portfolio optimization by field

ABSTRACT

Systems and methods are provided for generating a set of target seeds with optimal yield and risk performance. One example computer-implemented method includes generating, by a server, representative yield values for a group of seeds based on historical agricultural data, generating a dataset of risk values for the seeds, and generating a dataset of target seeds from the seeds for planting in one or more target fields based on: the dataset of risk values, the representative yield values, and properties for the target field(s). The method also includes generating, by the server, allocation instructions for the target seeds included in the dataset of target seeds, where the allocation instructions are indicative of, for each target seed, a planting quantity for the target seed and a planting location for the target seed within the target field(s).

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.15/807,876, filed Nov. 9, 2017. The entire disclosure of the aboveapplication is incorporated herein by reference.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyright orrights whatsoever. © 2015-2017 The Climate Corporation.

FIELD OF THE DISCLOSURE

The present disclosure relates to computer systems useful inagriculture. The present disclosure relates more specifically tocomputer systems that are programmed to use agricultural data related tohybrid seeds and one or more target fields to provide a set ofrecommended hybrid seeds identified to produce successful yield valuesthat exceed average yield values for the one or more target fields.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

A successful harvest depends on many factors including hybrid selection,soil fertilization, irrigation, and pest control which each contributeto the growth rate of corn plants. One of the most importantagricultural management factors is choosing which hybrid seeds to planton target fields. Varieties of hybrid seeds range from hybrids suitedfor short growth seasons to longer growth seasons, hotter or coldertemperatures, dryer or wetter climates, and different hybrids suited forspecific soil compositions. Achieving optimal performance for a specifichybrid seed depends on whether the field conditions align with theoptimal growing conditions for the specific hybrid seed. For example, aspecific corn hybrid may be rated to produce a specific amount of yieldfor a grower however, if the field conditions do not match the optimalconditions used to rate the specific corn hybrid it is unlikely that thecorn hybrid will meet the yield expectations for the grower.

Once a set of hybrid seeds are chosen for planting, a grower must thendetermine a planting strategy. Planting strategies include determiningthe amount and placement of each of the chosen hybrid seeds. Strategiesfor determining amount and placement may dictate whether harvest yieldmeet expectations. For example, planting hybrid seeds that have similarstrengths and vulnerabilities may result in a good yield if conditionsare favorable. However, if conditions fluctuate, such as receiving lessthan expected rainfall or experiencing higher than normal temperatures,then overall yield for similar hybrid seeds may be diminished. Adiversified planting strategy may be preferred to overcome unforeseenenvironmental fluctuations.

Techniques described herein help alleviate some of these issues and helpgrowers determine what seeds to plant in which fields.

SUMMARY

The appended claims may serve as a summary of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using agronomic data provided by one or more datasources.

FIG. 4 is a block diagram that illustrates a computer system upon whichan embodiment of the invention may be implemented.

FIG. 5 depicts an example embodiment of a timeline view for data entry.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry.

FIG. 7 depicts an example flowchart for generating a target successyield group of hybrid seeds identified for optimal yield performance ontarget fields based on agricultural data records of the hybrid seeds andgeo-location data associated with the target fields.

FIG. 8 depicts an example of different regions within a state that havedifferent assigned relative maturity based on the growing seasondurations.

FIG. 9 depicts a graph describing the range of normalized yield valuesfor hybrid seeds within a classified relative maturity.

FIG. 10 depicts an example flowchart for generating a set of targethybrid seeds identified for optimal yield performance and managed riskon target fields based on agricultural data records of the hybrid seedsand geo-location data associated with the target fields

FIG. 11 depicts an example graph of yield values versus risk values forone or more hybrid seeds.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,that embodiments may be practiced without these specific details. Inother instances, well-known structures and devices are shown in blockdiagram form in order to avoid unnecessarily obscuring the presentdisclosure. Embodiments are disclosed in sections according to thefollowing outline:

1. GENERAL OVERVIEW

2. EXAMPLE AGRICULTURAL INTELLIGENCE COMPUTER SYSTEM

-   -   2.1. STRUCTURAL OVERVIEW    -   2.2. APPLICATION PROGRAM OVERVIEW    -   2.3. DATA INGEST TO THE COMPUTER SYSTEM    -   2.4. PROCESS OVERVIEW—AGRONOMIC MODEL TRAINING    -   2.5. HYBRID SEED CLASSIFICATION SUBSYSTEM    -   2.6. HYBRID SEED RECOMMENDATION SUBSYSTEM    -   2.7. IMPLEMENTATION EXAMPLE—HARDWARE OVERVIEW

3. FUNCTIONAL OVERVIEW— GENERATE AND DISPLAY TARGET SUCCESS YIELD GROUPOF HYBRID SEEDS

-   -   3.1. DATA INPUT    -   3.2. AGRICULTURAL DATA PROCESSING    -   3.3. PRESENT TARGET SUCCESS YIELD GROUP

4. FUNCTIONAL OVERVIEW—GENERATE AND DISPLAY TARGET HYBRID SEEDS FORPLANTING

-   -   4.1. DATA INPUT    -   4.2. HYBRID SEED SELECTION    -   4.3. GENERATE RISK VALUES FOR HYBRID SEEDS    -   4.4. GENERATE DATASET OF TARGET HYBRID SEEDS    -   4.5. SEED PORTFOLIO ANALYSIS    -   4.6. PRESENT SET OF TARGET HYBRID SEEDS

1. General Overview

A computer system and a computer-implemented method that are disclosedherein for generating a set of target success yield group of hybridseeds that have a high probability of a successful yield on one or moretarget fields. In an embodiment, a target success yield group of hybridseeds may be generated using a server computer system that is configuredto receive, over a digital data communication network, one or moreagricultural data records that represent crop seed data describing seedand yield properties of one or more hybrid seeds and first fieldgeo-location data for one or more agricultural fields where the one ormore hybrid seeds were planted. The server computer system then receivessecond geo-locations data for one or more target fields where hybridseeds are to be planted.

The server computer system includes hybrid seed normalizationinstructions configured to generate a dataset of hybrid seed propertiesthat describe a representative yield value and an environmentalclassification for each hybrid seed from the one or more agriculturaldata records. Probability of success generation instructions on theserver computer system are configured to then generate a dataset ofsuccess probability scores that describe the probability of a successfulyield on the one or more target fields. A successful yield may bedefined as an estimated yield value for a specific hybrid seed for anenvironmental classification that exceeds the average yield for the sameenvironmental classification by a specific yield amount. The probabilityof success values for each hybrid seed are based upon the dataset ofhybrid seed properties and the second geo-location data for the one ormore target fields.

The server computer system includes yield classification instructionsconfigured to generate a target success yield group made up of a subsetof the one or more hybrid seeds and the probability of success valuesassociated with each of the subset of the one or more hybrid seeds.Generation of the target success yield group is based upon the datasetof success probability scores for each hybrid seed and a configuredsuccessful yield threshold, where hybrid seeds are added to the targetsuccess yield group if the probability of success value for a hybridseed exceeds the successful yield threshold.

The server computer system is configured to cause display, on a displaydevice communicatively coupled to the server computer system, of thetarget success yield group and yield values associated with each hybridseed in the target success yield group.

In an embodiment, the target success yield group (or another set ofseeds and fields) may be used to generate a set of target hybrid seedsselected for planting on the one or more target fields. The servercomputer system is configured to receive the target success yield groupof candidate hybrid seeds that may be candidates for planting on the oneor more target fields. Included in the target success yield group is theone or more hybrid seeds, the probability of success values associatedwith each of the one or more hybrid seeds that describe a probability ofa successful yield, and historical agricultural data associated witheach of the one or more hybrid seeds. The server computer then receivesproperty information related to the one or more target fields.

Hybrid seed filtering instructions within the server computer system areconfigured to select a subset of the hybrid seeds that have probabilityof success values greater than a target probability filtering threshold.The server computer system includes hybrid seed normalizationinstructions configured to generate representative yield values forhybrid seeds in the subset of the one or more hybrid seeds based on thehistorical agricultural data.

The server computer system includes risk generation instructionsconfigured to generate a dataset of risk values for the subset of theone or more hybrid seeds. The dataset of risk values describes riskassociated with each hybrid seed based on the historical agriculturaldata. The server computer system includes optimization classificationinstructions configured to generate a dataset of target hybrid seeds forplanting on the one or more target fields based on the dataset of riskvalues, the representative yield values for the subset of the one ormore hybrid seeds, and the one or more properties for the one or moretarget fields. The dataset of target hybrid seeds includes target hybridseeds that have the representative yield values that meet a specifictarget threshold for a range of risk values from the dataset of riskvalues across the one or more target fields.

The server computer system is configured to display, on the displaydevice communicatively coupled to the server computer system, thedataset of target hybrid seeds including the representative yield valuesand risk values from the dataset of risk values associated with eachtarget hybrid seed in the dataset of target hybrid seeds and the one ormore target fields.

2. Example Agricultural Intelligence Computer System

2.1 Structural Overview

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate. In oneembodiment, a user 102 owns, operates or possesses a field managercomputing device 104 in a field location or associated with a fieldlocation such as a field intended for agricultural activities or amanagement location for one or more agricultural fields. The fieldmanager computer device 104 is programmed or configured to provide fielddata 106 to an agricultural intelligence computer system 130 via one ormore networks 109.

Examples of field data 106 include (a) identification data (for example,acreage, field name, field identifiers, geographic identifiers, boundaryidentifiers, crop identifiers, and any other suitable data that may beused to identify farm land, such as a common land unit (CLU), lot andblock number, a parcel number, geographic coordinates and boundaries,Farm Serial Number (FSN), farm number, tract number, field number,section, township, and/or range), (b) harvest data (for example, croptype, crop variety, crop rotation, whether the crop is grownorganically, harvest date, Actual Production History (APH), expectedyield, yield, crop price, crop revenue, grain moisture, tillagepractice, and previous growing season information), (c) soil data (forexample, type, composition, pH, organic matter (OM), cation exchangecapacity (CEC)), (d) planting data (for example, planting date, seed(s)type, relative maturity (RM) of planted seed(s), seed population), (e)fertilizer data (for example, nutrient type (Nitrogen, Phosphorous,Potassium), application type, application date, amount, source, method),(f) chemical application data (for example, pesticide, herbicide,fungicide, other substance or mixture of substances intended for use asa plant regulator, defoliant, or desiccant, application date, amount,source, method), (g) irrigation data (for example, application date,amount, source, method), (h) weather data (for example, precipitation,rainfall rate, predicted rainfall, water runoff rate region,temperature, wind, forecast, pressure, visibility, clouds, heat index,dew point, humidity, snow depth, air quality, sunrise, sunset), (i)imagery data (for example, imagery and light spectrum information froman agricultural apparatus sensor, camera, computer, smartphone, tablet,unmanned aerial vehicle, planes or satellite), (j) scouting observations(photos, videos, free form notes, voice recordings, voicetranscriptions, weather conditions (temperature, precipitation (currentand over time), soil moisture, crop growth stage, wind velocity,relative humidity, dew point, black layer)), and (k) soil, seed, cropphenology, pest and disease reporting, and predictions sources anddatabases.

A data server computer 108 is communicatively coupled to agriculturalintelligence computer system 130 and is programmed or configured to sendexternal data 110 to agricultural intelligence computer system 130 viathe network(s) 109. The external data server computer 108 may be ownedor operated by the same legal person or entity as the agriculturalintelligence computer system 130, or by a different person or entitysuch as a government agency, non-governmental organization (NGO), and/ora private data service provider. Examples of external data includeweather data, imagery data, soil data, or statistical data relating tocrop yields, among others. External data 110 may consist of the sametype of information as field data 106. In some embodiments, the externaldata 110 is provided by an external data server 108 owned by the sameentity that owns and/or operates the agricultural intelligence computersystem 130. For example, the agricultural intelligence computer system130 may include a data server focused exclusively on a type of data thatmight otherwise be obtained from third party sources, such as weatherdata. In some embodiments, an external data server 108 may actually beincorporated within the system 130.

An agricultural apparatus 111 may have one or more remote sensors 112fixed thereon, which sensors are communicatively coupled either directlyor indirectly via agricultural apparatus 111 to the agriculturalintelligence computer system 130 and are programmed or configured tosend sensor data to agricultural intelligence computer system 130.Examples of agricultural apparatus 111 include tractors, combines,harvesters, planters, trucks, fertilizer equipment, aerial vehiclesincluding unmanned aerial vehicles, and any other item of physicalmachinery or hardware, typically mobile machinery, and which may be usedin tasks associated with agriculture. In some embodiments, a single unitof apparatus 111 may comprise a plurality of sensors 112 that arecoupled locally in a network on the apparatus; controller area network(CAN) is example of such a network that can be installed in combines,harvesters, sprayers, and cultivators. Application controller 114 iscommunicatively coupled to agricultural intelligence computer system 130via the network(s) 109 and is programmed or configured to receive one ormore scripts that are used to control an operating parameter of anagricultural vehicle or implement from the agricultural intelligencecomputer system 130. For instance, a controller area network (CAN) businterface may be used to enable communications from the agriculturalintelligence computer system 130 to the agricultural apparatus 111, suchas how the CLIMATE FIELDVIEW DRIVE, available from The ClimateCorporation, San Francisco, Calif., is used. Sensor data may consist ofthe same type of information as field data 106. In some embodiments,remote sensors 112 may not be fixed to an agricultural apparatus 111 butmay be remotely located in the field and may communicate with network109.

The apparatus 111 may comprise a cab computer 115 that is programmedwith a cab application, which may comprise a version or variant of themobile application for device 104 that is further described in othersections herein. In an embodiment, cab computer 115 comprises a compactcomputer, often a tablet-sized computer or smartphone, with a graphicalscreen display, such as a color display, that is mounted within anoperator's cab of the apparatus 111. Cab computer 115 may implement someor all of the operations and functions that are described further hereinfor the mobile computer device 104.

The network(s) 109 broadly represent any combination of one or more datacommunication networks including local area networks, wide areanetworks, internetworks or internets, using any of wireline or wirelesslinks, including terrestrial or satellite links. The network(s) may beimplemented by any medium or mechanism that provides for the exchange ofdata between the various elements of FIG. 1 . The various elements ofFIG. 1 may also have direct (wired or wireless) communications links.The sensors 112, controller 114, external data server computer 108, andother elements of the system each comprise an interface compatible withthe network(s) 109 and are programmed or configured to use standardizedprotocols for communication across the networks such as TCP/IP,Bluetooth, CAN protocol and higher-layer protocols such as HTTP, TLS,and the like.

Agricultural intelligence computer system 130 is programmed orconfigured to receive field data 106 from field manager computing device104, external data 110 from external data server computer 108, andsensor data from remote sensor 112. Agricultural intelligence computersystem 130 may be further configured to host, use or execute one or morecomputer programs, other software elements, digitally programmed logicsuch as FPGAs or ASICs, or any combination thereof to performtranslation and storage of data values, construction of digital modelsof one or more crops on one or more fields, generation ofrecommendations and notifications, and generation and sending of scriptsto application controller 114, in the manner described further in othersections of this disclosure.

In an embodiment, agricultural intelligence computer system 130 isprogrammed with or comprises a communication layer 132, presentationlayer 134, data management layer 140, hardware/virtualization layer 150,and model and field data repository 160. “Layer,” in this context,refers to any combination of electronic digital interface circuits,microcontrollers, firmware such as drivers, and/or computer programs orother software elements.

Communication layer 132 may be programmed or configured to performinput/output interfacing functions including sending requests to fieldmanager computing device 104, external data server computer 108, andremote sensor 112 for field data, external data, and sensor datarespectively. Communication layer 132 may be programmed or configured tosend the received data to model and field data repository 160 to bestored as field data 106.

Presentation layer 134 may be programmed or configured to generate agraphical user interface (GUI) to be displayed on field managercomputing device 104, cab computer 115 or other computers that arecoupled to the system 130 through the network 109. The GUI may comprisecontrols for inputting data to be sent to agricultural intelligencecomputer system 130, generating requests for models and/orrecommendations, and/or displaying recommendations, notifications,models, and other field data.

Data management layer 140 may be programmed or configured to manage readoperations and write operations involving the repository 160 and otherfunctional elements of the system, including queries and result setscommunicated between the functional elements of the system and therepository. Examples of data management layer 140 include JDBC, SQLserver interface code, and/or HADOOP interface code, among others.Repository 160 may comprise a database. As used herein, the term“database” may refer to either a body of data, a relational databasemanagement system (RDBMS), or to both. As used herein, a database maycomprise any collection of data including hierarchical databases,relational databases, flat file databases, object-relational databases,object oriented databases, distributed databases, and any otherstructured collection of records or data that is stored in a computersystem. Examples of RDBMS's include, but are not limited to including,ORACLE®, MYSQL, IBM® DB2, MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQLdatabases. However, any database may be used that enables the systemsand methods described herein.

When field data 106 is not provided directly to the agriculturalintelligence computer system via one or more agricultural machines oragricultural machine devices that interacts with the agriculturalintelligence computer system, the user may be prompted via one or moreuser interfaces on the user device (served by the agriculturalintelligence computer system) to input such information. In an exampleembodiment, the user may specify identification data by accessing a mapon the user device (served by the agricultural intelligence computersystem) and selecting specific CLUs that have been graphically shown onthe map. In an alternative embodiment, the user 102 may specifyidentification data by accessing a map on the user device (served by theagricultural intelligence computer system 130) and drawing boundaries ofthe field over the map. Such CLU selection or map drawings representgeographic identifiers. In alternative embodiments, the user may specifyidentification data by accessing field identification data (provided asshape files or in a similar format) from the U. S. Department ofAgriculture Farm Service Agency or other source via the user device andproviding such field identification data to the agriculturalintelligence computer system.

In an example embodiment, the agricultural intelligence computer system130 is programmed to generate and cause displaying a graphical userinterface comprising a data manager for data input. After one or morefields have been identified using the methods described above, the datamanager may provide one or more graphical user interface widgets whichwhen selected can identify changes to the field, soil, crops, tillage,or nutrient practices. The data manager may include a timeline view, aspreadsheet view, and/or one or more editable programs.

FIG. 5 depicts an example embodiment of a timeline view for data entry.Using the display depicted in FIG. 5 , a user computer can input aselection of a particular field and a particular date for the additionof event. Events depicted at the top of the timeline may includeNitrogen, Planting, Practices, and Soil. To add a nitrogen applicationevent, a user computer may provide input to select the nitrogen tab. Theuser computer may then select a location on the timeline for aparticular field in order to indicate an application of nitrogen on theselected field. In response to receiving a selection of a location onthe timeline for a particular field, the data manager may display a dataentry overlay, allowing the user computer to input data pertaining tonitrogen applications, planting procedures, soil application, tillageprocedures, irrigation practices, or other information relating to theparticular field. For example, if a user computer selects a portion ofthe timeline and indicates an application of nitrogen, then the dataentry overlay may include fields for inputting an amount of nitrogenapplied, a date of application, a type of fertilizer used, and any otherinformation related to the application of nitrogen.

In an embodiment, the data manager provides an interface for creatingone or more programs. “Program,” in this context, refers to a set ofdata pertaining to nitrogen applications, planting procedures, soilapplication, tillage procedures, irrigation practices, or otherinformation that may be related to one or more fields, and that can bestored in digital data storage for reuse as a set in other operations.After a program has been created, it may be conceptually applied to oneor more fields and references to the program may be stored in digitalstorage in association with data identifying the fields. Thus, insteadof manually entering identical data relating to the same nitrogenapplications for multiple different fields, a user computer may create aprogram that indicates a particular application of nitrogen and thenapply the program to multiple different fields. For example, in thetimeline view of FIG. 5 , the top two timelines have the “Springapplied” program selected, which includes an application of 150 lbs.N/ac in early April. The data manager may provide an interface forediting a program. In an embodiment, when a particular program isedited, each field that has selected the particular program is edited.For example, in FIG. 5 , if the “Spring applied” program is edited toreduce the application of nitrogen to 130 lbs. N/ac, the top two fieldsmay be updated with a reduced application of nitrogen based on theedited program.

In an embodiment, in response to receiving edits to a field that has aprogram selected, the data manager removes the correspondence of thefield to the selected program. For example, if a nitrogen application isadded to the top field in FIG. 5 , the interface may update to indicatethat the “Spring applied” program is no longer being applied to the topfield. While the nitrogen application in early April may remain, updatesto the “Spring applied” program would not alter the April application ofnitrogen.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry. Using the display depicted in FIG. 6 , a user can create and editinformation for one or more fields. The data manager may includespreadsheets for inputting information with respect to Nitrogen,Planting, Practices, and Soil as depicted in FIG. 6 . To edit aparticular entry, a user computer may select the particular entry in thespreadsheet and update the values. For example, FIG. 6 depicts anin-progress update to a target yield value for the second field.Additionally, a user computer may select one or more fields in order toapply one or more programs. In response to receiving a selection of aprogram for a particular field, the data manager may automaticallycomplete the entries for the particular field based on the selectedprogram. As with the timeline view, the data manager may update theentries for each field associated with a particular program in responseto receiving an update to the program. Additionally, the data managermay remove the correspondence of the selected program to the field inresponse to receiving an edit to one of the entries for the field.

In an embodiment, model and field data is stored in model and field datarepository 160. Model data comprises data models created for one or morefields. For example, a crop model may include a digitally constructedmodel of the development of a crop on the one or more fields. “Model,”in this context, refers to an electronic digitally stored set ofexecutable instructions and data values, associated with one another,which are capable of receiving and responding to a programmatic or otherdigital call, invocation, or request for resolution based upon specifiedinput values, to yield one or more stored or calculated output valuesthat can serve as the basis of computer-implemented recommendations,output data displays, or machine control, among other things. Persons ofskill in the field find it convenient to express models usingmathematical equations, but that form of expression does not confine themodels disclosed herein to abstract concepts; instead, each model hereinhas a practical application in a computer in the form of storedexecutable instructions and data that implement the model using thecomputer. The model may include a model of past events on the one ormore fields, a model of the current status of the one or more fields,and/or a model of predicted events on the one or more fields. Model andfield data may be stored in data structures in memory, rows in adatabase table, in flat files or spreadsheets, or other forms of storeddigital data.

In an embodiment, a hybrid seed classification subsystem 170 containsspecially configured logic, including, but not limited to, hybrid seednormalization instructions 172, probability of success generationinstructions 174, and yield classification instructions 176 comprises aset of one or more pages of main memory, such as RAM, in theagricultural intelligence computer system 130 into which executableinstructions have been loaded and which when executed cause theagricultural intelligence computing system to perform the functions oroperations that are described herein with reference to those modules. Inan embodiment, a hybrid seed recommendation subsystem 180 containsspecially configured logic, including, but not limited to, hybrid seedfiltering instructions 182, risk generation instructions 184, andoptimization classification instructions 186 comprises a set of one ormore pages of main memory, such as RAM, in the agricultural intelligencecomputer system 130 into which executable instructions have been loadedand which when executed cause the agricultural intelligence computingsystem to perform the functions or operations that are described hereinwith reference to those modules. For example, the hybrid seednormalization instructions 172 may comprise a set of pages in RAM thatcontain instructions which when executed cause performing the targetidentification functions that are described herein. The instructions maybe in machine executable code in the instruction set of a CPU and mayhave been compiled based upon source code written in JAVA, C, C++,OBJECTIVE-C, or any other human-readable programming language orenvironment, alone or in combination with scripts in JAVASCRIPT, otherscripting languages and other programming source text. The term “pages”is intended to refer broadly to any region within main memory and thespecific terminology used in a system may vary depending on the memoryarchitecture or processor architecture. In another embodiment, each ofhybrid seed normalization instructions 172, probability of successgeneration instructions 174, yield classification instructions 176,hybrid seed filtering instructions 182, risk generation instructions184, and optimization classification instructions 186 also may representone or more files or projects of source code that are digitally storedin a mass storage device such as non-volatile RAM or disk storage, inthe agricultural intelligence computer system 130 or a separaterepository system, which when compiled or interpreted cause generatingexecutable instructions which when executed cause the agriculturalintelligence computing system to perform the functions or operationsthat are described herein with reference to those modules. In otherwords, the drawing figure may represent the manner in which programmersor software developers organize and arrange source code for latercompilation into an executable, or interpretation into bytecode or theequivalent, for execution by the agricultural intelligence computersystem 130.

Hardware/virtualization layer 150 comprises one or more centralprocessing units (CPUs), memory controllers, and other devices,components, or elements of a computer system such as volatile ornon-volatile memory, non-volatile storage such as disk, and I/O devicesor interfaces as illustrated and described, for example, in connectionwith FIG. 4 . The layer 150 also may comprise programmed instructionsthat are configured to support virtualization, containerization, orother technologies.

For purposes of illustrating a clear example, FIG. 1 shows a limitednumber of instances of certain functional elements. However, in otherembodiments, there may be any number of such elements. For example,embodiments may use thousands or millions of different mobile computingdevices 104 associated with different users. Further, the system 130and/or external data server computer 108 may be implemented using two ormore processors, cores, clusters, or instances of physical machines orvirtual machines, configured in a discrete location or co-located withother elements in a datacenter, shared computing facility or cloudcomputing facility.

2.2. Application Program Overview

In an embodiment, the implementation of the functions described hereinusing one or more computer programs or other software elements that areloaded into and executed using one or more general-purpose computerswill cause the general-purpose computers to be configured as aparticular machine or as a computer that is specially adapted to performthe functions described herein. Further, each of the flow diagrams thatare described further herein may serve, alone or in combination with thedescriptions of processes and functions in prose herein, as algorithms,plans or directions that may be used to program a computer or logic toimplement the functions that are described. In other words, all theprose text herein, and all the drawing figures, together are intended toprovide disclosure of algorithms, plans or directions that aresufficient to permit a skilled person to program a computer to performthe functions that are described herein, in combination with the skilland knowledge of such a person given the level of skill that isappropriate for inventions and disclosures of this type.

In an embodiment, user 102 interacts with agricultural intelligencecomputer system 130 using field manager computing device 104 configuredwith an operating system and one or more application programs or apps;the field manager computing device 104 also may interoperate with theagricultural intelligence computer system independently andautomatically under program control or logical control and direct userinteraction is not always required. Field manager computing device 104broadly represents one or more of a smart phone, PDA, tablet computingdevice, laptop computer, desktop computer, workstation, or any othercomputing device capable of transmitting and receiving information andperforming the functions described herein. Field manager computingdevice 104 may communicate via a network using a mobile applicationstored on field manager computing device 104, and in some embodiments,the device may be coupled using a cable 113 or connector to the sensor112 and/or controller 114. A particular user 102 may own, operate orpossess and use, in connection with system 130, more than one fieldmanager computing device 104 at a time.

The mobile application may provide client-side functionality, via thenetwork to one or more mobile computing devices. In an exampleembodiment, field manager computing device 104 may access the mobileapplication via a web browser or a local client application or app.Field manager computing device 104 may transmit data to, and receivedata from, one or more front-end servers, using web-based protocols orformats such as HTTP, XML and/or JSON, or app-specific protocols. In anexample embodiment, the data may take the form of requests and userinformation input, such as field data, into the mobile computing device.In some embodiments, the mobile application interacts with locationtracking hardware and software on field manager computing device 104which determines the location of field manager computing device 104using standard tracking techniques such as multilateration of radiosignals, the global positioning system (GPS), WiFi positioning systems,or other methods of mobile positioning. In some cases, location data orother data associated with the device 104, user 102, and/or useraccount(s) may be obtained by queries to an operating system of thedevice or by requesting an app on the device to obtain data from theoperating system.

In an embodiment, field manager computing device 104 sends field data106 to agricultural intelligence computer system 130 comprising orincluding, but not limited to, data values representing one or more of:a geographical location of the one or more fields, tillage informationfor the one or more fields, crops planted in the one or more fields, andsoil data extracted from the one or more fields. Field manager computingdevice 104 may send field data 106 in response to user input from user102 specifying the data values for the one or more fields. Additionally,field manager computing device 104 may automatically send field data 106when one or more of the data values becomes available to field managercomputing device 104. For example, field manager computing device 104may be communicatively coupled to remote sensor 112 and/or applicationcontroller 114 which include an irrigation sensor and/or irrigationcontroller. In response to receiving data indicating that applicationcontroller 114 released water onto the one or more fields, field managercomputing device 104 may send field data 106 to agriculturalintelligence computer system 130 indicating that water was released onthe one or more fields. Field data 106 identified in this disclosure maybe input and communicated using electronic digital data that iscommunicated between computing devices using parameterized URLs overHTTP, or another suitable communication or messaging protocol.

A commercial example of the mobile application is CLIMATE FIELDVIEW,commercially available from The Climate Corporation, San Francisco,Calif. The CLIMATE FIELDVIEW application, or other applications, may bemodified, extended, or adapted to include features, functions, andprogramming that have not been disclosed earlier than the filing date ofthis disclosure. In one embodiment, the mobile application comprises anintegrated software platform that allows a grower to make fact-baseddecisions for their operation because it combines historical data aboutthe grower's fields with any other data that the grower wishes tocompare. The combinations and comparisons may be performed in real timeand are based upon scientific models that provide potential scenarios topermit the grower to make better, more informed decisions.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution. In FIG. 2 , each named element represents a regionof one or more pages of RAM or other main memory, or one or more blocksof disk storage or other non-volatile storage, and the programmedinstructions within those regions. In one embodiment, in view (a), amobile computer application 200 comprises account-fields-dataingestion-sharing instructions 202, overview and alert instructions 204,digital map book instructions 206, seeds and planting instructions 208,nitrogen instructions 210, weather instructions 212, field healthinstructions 214, and performance instructions 216.

In one embodiment, a mobile computer application 200 comprises account,fields, data ingestion, sharing instructions 202 which are programmed toreceive, translate, and ingest field data from third party systems viamanual upload or APIs. Data types may include field boundaries, yieldmaps, as-planted maps, soil test results, as-applied maps, and/ormanagement zones, among others. Data formats may include shape files,native data formats of third parties, and/or farm management informationsystem (FMIS) exports, among others. Receiving data may occur via manualupload, e-mail with attachment, external APIs that push data to themobile application, or instructions that call APIs of external systemsto pull data into the mobile application. In one embodiment, mobilecomputer application 200 comprises a data inbox. In response toreceiving a selection of the data inbox, the mobile computer application200 may display a graphical user interface for manually uploading datafiles and importing uploaded files to a data manager.

In one embodiment, digital map book instructions 206 comprise field mapdata layers stored in device memory and are programmed with datavisualization tools and geospatial field notes. This provides growerswith convenient information close at hand for reference, logging andvisual insights into field performance. In one embodiment, overview andalert instructions 204 are programmed to provide an operation-wide viewof what is important to the grower, and timely recommendations to takeaction or focus on particular issues. This permits the grower to focustime on what needs attention, to save time and preserve yield throughoutthe season. In one embodiment, seeds and planting instructions 208 areprogrammed to provide tools for seed selection, hybrid placement, andscript creation, including variable rate (VR) script creation, basedupon scientific models and empirical data. This enables growers tomaximize yield or return on investment through optimized seed purchase,placement and population.

In one embodiment, script generation instructions 205 are programmed toprovide an interface for generating scripts, including variable rate(VR) fertility scripts. The interface enables growers to create scriptsfor field implements, such as nutrient applications, planting, andirrigation. For example, a planting script interface may comprise toolsfor identifying a type of seed for planting. Upon receiving a selectionof the seed type, mobile computer application 200 may display one ormore fields broken into management zones, such as the field map datalayers created as part of digital map book instructions 206. In oneembodiment, the management zones comprise soil zones along with a panelidentifying each soil zone and a soil name, texture, drainage for eachzone, or other field data. Mobile computer application 200 may alsodisplay tools for editing or creating such, such as graphical tools fordrawing management zones, such as soil zones, over a map of one or morefields. Planting procedures may be applied to all management zones ordifferent planting procedures may be applied to different subsets ofmanagement zones. When a script is created, mobile computer application200 may make the script available for download in a format readable byan application controller, such as an archived or compressed format.Additionally, and/or alternatively, a script may be sent directly to cabcomputer 115 from mobile computer application 200 and/or uploaded to oneor more data servers and stored for further use.

In one embodiment, nitrogen instructions 210 are programmed to providetools to inform nitrogen decisions by visualizing the availability ofnitrogen to crops. This enables growers to maximize yield or return oninvestment through optimized nitrogen application during the season.Example programmed functions include displaying images such as SSURGOimages to enable drawing of fertilizer application zones and/or imagesgenerated from subfield soil data, such as data obtained from sensors,at a high spatial resolution (as fine as millimeters or smallerdepending on sensor proximity and resolution); upload of existinggrower-defined zones; providing a graph of plant nutrient availabilityand/or a map to enable tuning application(s) of nitrogen across multiplezones; output of scripts to drive machinery; tools for mass data entryand adjustment; and/or maps for data visualization, among others. “Massdata entry,” in this context, may mean entering data once and thenapplying the same data to multiple fields and/or zones that have beendefined in the system; example data may include nitrogen applicationdata that is the same for many fields and/or zones of the same grower,but such mass data entry applies to the entry of any type of field datainto the mobile computer application 200. For example, nitrogeninstructions 210 may be programmed to accept definitions of nitrogenapplication and practices programs and to accept user input specifyingto apply those programs across multiple fields. “Nitrogen applicationprograms,” in this context, refers to stored, named sets of data thatassociates: a name, color code or other identifier, one or more dates ofapplication, types of material or product for each of the dates andamounts, method of application or incorporation such as injected orbroadcast, and/or amounts or rates of application for each of the dates,crop or hybrid that is the subject of the application, among others.“Nitrogen practices programs,” in this context, refer to stored, namedsets of data that associates: a practices name; a previous crop; atillage system; a date of primarily tillage; one or more previoustillage systems that were used; one or more indicators of applicationtype, such as manure, that were used. Nitrogen instructions 210 also maybe programmed to generate and cause displaying a nitrogen graph, whichindicates projections of plant use of the specified nitrogen and whethera surplus or shortfall is predicted; in some embodiments, differentcolor indicators may signal a magnitude of surplus or magnitude ofshortfall. In one embodiment, a nitrogen graph comprises a graphicaldisplay in a computer display device comprising a plurality of rows,each row associated with and identifying a field; data specifying whatcrop is planted in the field, the field size, the field location, and agraphic representation of the field perimeter; in each row, a timelineby month with graphic indicators specifying each nitrogen applicationand amount at points correlated to month names; and numeric and/orcolored indicators of surplus or shortfall, in which color indicatesmagnitude.

In one embodiment, the nitrogen graph may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen graph. The user may then use his optimized nitrogen graph andthe related nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. Nitrogeninstructions 210 also may be programmed to generate and cause displayinga nitrogen map, which indicates projections of plant use of thespecified nitrogen and whether a surplus or shortfall is predicted; insome embodiments, different color indicators may signal a magnitude ofsurplus or magnitude of shortfall. The nitrogen map may displayprojections of plant use of the specified nitrogen and whether a surplusor shortfall is predicted for different times in the past and the future(such as daily, weekly, monthly or yearly) using numeric and/or coloredindicators of surplus or shortfall, in which color indicates magnitude.In one embodiment, the nitrogen map may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen map, such as to obtain a preferred amount of surplus toshortfall. The user may then use his optimized nitrogen map and therelated nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. In otherembodiments, similar instructions to the nitrogen instructions 210 couldbe used for application of other nutrients (such as phosphorus andpotassium), application of pesticide, and irrigation programs.

In one embodiment, weather instructions 212 are programmed to providefield-specific recent weather data and forecasted weather information.This enables growers to save time and have an efficient integrateddisplay with respect to daily operational decisions.

In one embodiment, field health instructions 214 are programmed toprovide timely remote sensing images highlighting in-season cropvariation and potential concerns. Example programmed functions includecloud checking, to identify possible clouds or cloud shadows;determining nitrogen indices based on field images; graphicalvisualization of scouting layers, including, for example, those relatedto field health, and viewing and/or sharing of scouting notes; and/ordownloading satellite images from multiple sources and prioritizing theimages for the grower, among others.

In one embodiment, performance instructions 216 are programmed toprovide reports, analysis, and insight tools using on-farm data forevaluation, insights and decisions. This enables the grower to seekimproved outcomes for the next year through fact-based conclusions aboutwhy return on investment was at prior levels, and insight intoyield-limiting factors. The performance instructions 216 may beprogrammed to communicate via the network(s) 109 to back-end analyticsprograms executed at agricultural intelligence computer system 130and/or external data server computer 108 and configured to analyzemetrics such as yield, yield differential, hybrid, population, SSURGOzone, soil test properties, or elevation, among others. Programmedreports and analysis may include yield variability analysis, treatmenteffect estimation, benchmarking of yield and other metrics against othergrowers based on anonymized data collected from many growers, or datafor seeds and planting, among others.

Applications having instructions configured in this way may beimplemented for different computing device platforms while retaining thesame general user interface appearance. For example, the mobileapplication may be programmed for execution on tablets, smartphones, orserver computers that are accessed using browsers at client computers.Further, the mobile application as configured for tablet computers orsmartphones may provide a full app experience or a cab app experiencethat is suitable for the display and processing capabilities of cabcomputer 115. For example, referring now to view (b) of FIG. 2 , in oneembodiment a cab computer application 220 may comprise maps-cabinstructions 222, remote view instructions 224, data collect andtransfer instructions 226, machine alerts instructions 228, scripttransfer instructions 230, and scouting-cab instructions 232. The codebase for the instructions of view (b) may be the same as for view (a)and executables implementing the code may be programmed to detect thetype of platform on which they are executing and to expose, through agraphical user interface, only those functions that are appropriate to acab platform or full platform. This approach enables the system torecognize the distinctly different user experience that is appropriatefor an in-cab environment and the different technology environment ofthe cab. The maps-cab instructions 222 may be programmed to provide mapviews of fields, farms or regions that are useful in directing machineoperation. The remote view instructions 224 may be programmed to turnon, manage, and provide views of machine activity in real-time or nearreal-time to other computing devices connected to the system 130 viawireless networks, wired connectors or adapters, and the like. The datacollect and transfer instructions 226 may be programmed to turn on,manage, and provide transfer of data collected at sensors andcontrollers to the system 130 via wireless networks, wired connectors oradapters, and the like. The machine alerts instructions 228 may beprogrammed to detect issues with operations of the machine or tools thatare associated with the cab and generate operator alerts. The scripttransfer instructions 230 may be configured to transfer in scripts ofinstructions that are configured to direct machine operations or thecollection of data. The scouting-cab instructions 232 may be programmedto display location-based alerts and information received from thesystem 130 based on the location of the field manager computing device104, agricultural apparatus 111, or sensors 112 in the field and ingest,manage, and provide transfer of location-based scouting observations tothe system 130 based on the location of the agricultural apparatus 111or sensors 112 in the field.

2.3. Data Ingest to the Computer System

In an embodiment, external data server computer 108 stores external data110, including soil data representing soil composition for the one ormore fields and weather data representing temperature and precipitationon the one or more fields. The weather data may include past and presentweather data as well as forecasts for future weather data. In anembodiment, external data server computer 108 comprises a plurality ofservers hosted by different entities. For example, a first server maycontain soil composition data while a second server may include weatherdata. Additionally, soil composition data may be stored in multipleservers. For example, one server may store data representing percentageof sand, silt, and clay in the soil while a second server may store datarepresenting percentage of organic matter (OM) in the soil.

In an embodiment, remote sensor 112 comprises one or more sensors thatare programmed or configured to produce one or more observations. Remotesensor 112 may be aerial sensors, such as satellites, vehicle sensors,planting equipment sensors, tillage sensors, fertilizer or insecticideapplication sensors, harvester sensors, and any other implement capableof receiving data from the one or more fields. In an embodiment,application controller 114 is programmed or configured to receiveinstructions from agricultural intelligence computer system 130.Application controller 114 may also be programmed or configured tocontrol an operating parameter of an agricultural vehicle or implement.For example, an application controller may be programmed or configuredto control an operating parameter of a vehicle, such as a tractor,planting equipment, tillage equipment, fertilizer or insecticideequipment, harvester equipment, or other farm implements such as a watervalve. Other embodiments may use any combination of sensors andcontrollers, of which the following are merely selected examples.

The system 130 may obtain or ingest data under user 102 control, on amass basis from a large number of growers who have contributed data to ashared database system. This form of obtaining data may be termed“manual data ingest” as one or more user-controlled computer operationsare requested or triggered to obtain data for use by the system 130. Asan example, the CLIMATE FIELDVIEW application, commercially availablefrom The Climate Corporation, San Francisco, Calif., may be operated toexport data to system 130 for storing in the repository 160.

For example, seed monitor systems can both control planter apparatuscomponents and obtain planting data, including signals from seed sensorsvia a signal harness that comprises a CAN backbone and point-to-pointconnections for registration and/or diagnostics. Seed monitor systemscan be programmed or configured to display seed spacing, population andother information to the user via the cab computer 115 or other deviceswithin the system 130. Examples are disclosed in U.S. Pat. No. 8,738,243and US Pat. Pub. 20150094916, and the present disclosure assumesknowledge of those other patent disclosures.

Likewise, yield monitor systems may contain yield sensors for harvesterapparatus that send yield measurement data to the cab computer 115 orother devices within the system 130. Yield monitor systems may utilizeone or more remote sensors 112 to obtain grain moisture measurements ina combine or other harvester and transmit these measurements to the uservia the cab computer 115 or other devices within the system 130.

In an embodiment, examples of sensors 112 that may be used with anymoving vehicle or apparatus of the type described elsewhere hereininclude kinematic sensors and position sensors. Kinematic sensors maycomprise any of speed sensors such as radar or wheel speed sensors,accelerometers, or gyros. Position sensors may comprise GPS receivers ortransceivers, or WiFi-based position or mapping apps that are programmedto determine location based upon nearby WiFi hotspots, among others.

In an embodiment, examples of sensors 112 that may be used with tractorsor other moving vehicles include engine speed sensors, fuel consumptionsensors, area counters or distance counters that interact with GPS orradar signals, PTO (power take-off) speed sensors, tractor hydraulicssensors configured to detect hydraulics parameters such as pressure orflow, and/or and hydraulic pump speed, wheel speed sensors or wheelslippage sensors. In an embodiment, examples of controllers 114 that maybe used with tractors include hydraulic directional controllers,pressure controllers, and/or flow controllers; hydraulic pump speedcontrollers; speed controllers or governors; hitch position controllers;or wheel position controllers provide automatic steering.

In an embodiment, examples of sensors 112 that may be used with seedplanting equipment such as planters, drills, or air seeders include seedsensors, which may be optical, electromagnetic, or impact sensors;downforce sensors such as load pins, load cells, pressure sensors; soilproperty sensors such as reflectivity sensors, moisture sensors,electrical conductivity sensors, optical residue sensors, or temperaturesensors; component operating criteria sensors such as planting depthsensors, downforce cylinder pressure sensors, seed disc speed sensors,seed drive motor encoders, seed conveyor system speed sensors, or vacuumlevel sensors; or pesticide application sensors such as optical or otherelectromagnetic sensors, or impact sensors. In an embodiment, examplesof controllers 114 that may be used with such seed planting equipmentinclude: toolbar fold controllers, such as controllers for valvesassociated with hydraulic cylinders; downforce controllers, such ascontrollers for valves associated with pneumatic cylinders, airbags, orhydraulic cylinders, and programmed for applying downforce to individualrow units or an entire planter frame; planting depth controllers, suchas linear actuators; metering controllers, such as electric seed meterdrive motors, hydraulic seed meter drive motors, or swath controlclutches; hybrid selection controllers, such as seed meter drive motors,or other actuators programmed for selectively allowing or preventingseed or an air-seed mixture from delivering seed to or from seed metersor central bulk hoppers; metering controllers, such as electric seedmeter drive motors, or hydraulic seed meter drive motors; seed conveyorsystem controllers, such as controllers for a belt seed deliveryconveyor motor; marker controllers, such as a controller for a pneumaticor hydraulic actuator; or pesticide application rate controllers, suchas metering drive controllers, orifice size or position controllers.

In an embodiment, examples of sensors 112 that may be used with tillageequipment include position sensors for tools such as shanks or discs;tool position sensors for such tools that are configured to detectdepth, gang angle, or lateral spacing; downforce sensors; or draft forcesensors. In an embodiment, examples of controllers 114 that may be usedwith tillage equipment include downforce controllers or tool positioncontrollers, such as controllers configured to control tool depth, gangangle, or lateral spacing.

In an embodiment, examples of sensors 112 that may be used in relationto apparatus for applying fertilizer, insecticide, fungicide and thelike, such as on-planter starter fertilizer systems, subsoil fertilizerapplicators, or fertilizer sprayers, include: fluid system criteriasensors, such as flow sensors or pressure sensors; sensors indicatingwhich spray head valves or fluid line valves are open; sensorsassociated with tanks, such as fill level sensors; sectional orsystem-wide supply line sensors, or row-specific supply line sensors; orkinematic sensors such as accelerometers disposed on sprayer booms. Inan embodiment, examples of controllers 114 that may be used with suchapparatus include pump speed controllers; valve controllers that areprogrammed to control pressure, flow, direction, PWM and the like; orposition actuators, such as for boom height, subsoiler depth, or boomposition.

In an embodiment, examples of sensors 112 that may be used withharvesters include yield monitors, such as impact plate strain gauges orposition sensors, capacitive flow sensors, load sensors, weight sensors,or torque sensors associated with elevators or augers, or optical orother electromagnetic grain height sensors; grain moisture sensors, suchas capacitive sensors; grain loss sensors, including impact, optical, orcapacitive sensors; header operating criteria sensors such as headerheight, header type, deck plate gap, feeder speed, and reel speedsensors; separator operating criteria sensors, such as concaveclearance, rotor speed, shoe clearance, or chaffer clearance sensors;auger sensors for position, operation, or speed; or engine speedsensors. In an embodiment, examples of controllers 114 that may be usedwith harvesters include header operating criteria controllers forelements such as header height, header type, deck plate gap, feederspeed, or reel speed; separator operating criteria controllers forfeatures such as concave clearance, rotor speed, shoe clearance, orchaffer clearance; or controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 that may be used with graincarts include weight sensors, or sensors for auger position, operation,or speed. In an embodiment, examples of controllers 114 that may be usedwith grain carts include controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 and controllers 114 may beinstalled in unmanned aerial vehicle (UAV) apparatus or “drones.” Suchsensors may include cameras with detectors effective for any range ofthe electromagnetic spectrum including visible light, infrared,ultraviolet, near-infrared (NIR), and the like; accelerometers;altimeters; temperature sensors; humidity sensors; pitot tube sensors orother airspeed or wind velocity sensors; battery life sensors; or radaremitters and reflected radar energy detection apparatus; otherelectromagnetic radiation emitters and reflected electromagneticradiation detection apparatus. Such controllers may include guidance ormotor control apparatus, control surface controllers, cameracontrollers, or controllers programmed to turn on, operate, obtain datafrom, manage and configure any of the foregoing sensors. Examples aredisclosed in U.S. patent application Ser. No. 14/831,165 and the presentdisclosure assumes knowledge of that other patent disclosure.

In an embodiment, sensors 112 and controllers 114 may be affixed to soilsampling and measurement apparatus that is configured or programmed tosample soil and perform soil chemistry tests, soil moisture tests, andother tests pertaining to soil. For example, the apparatus disclosed inU.S. Pat. Nos. 8,767,194 and 8,712,148 may be used, and the presentdisclosure assumes knowledge of those patent disclosures.

In an embodiment, sensors 112 and controllers 114 may comprise weatherdevices for monitoring weather conditions of fields. For example, theapparatus disclosed in U.S. Provisional Application No. 62/154,207,filed on Apr. 29, 2015, U.S. Provisional Application No. 62/175,160,filed on Jun. 12, 2015, U.S. Provisional Application No. 62/198,060,filed on Jul. 28, 2015, and U.S. Provisional Application No. 62/220,852,filed on Sep. 18, 2015, may be used, and the present disclosure assumesknowledge of those patent disclosures.

2.4. Process Overview-Agronomic Model Training

In an embodiment, the agricultural intelligence computer system 130 isprogrammed or configured to create an agronomic model. In this context,an agronomic model is a data structure in memory of the agriculturalintelligence computer system 130 that comprises field data 106, such asidentification data and harvest data for one or more fields. Theagronomic model may also comprise calculated agronomic properties whichdescribe either conditions which may affect the growth of one or morecrops on a field, or properties of the one or more crops, or both.Additionally, an agronomic model may comprise recommendations based onagronomic factors such as crop recommendations, irrigationrecommendations, planting recommendations, fertilizer recommendations,fungicide recommendations, pesticide recommendations, harvestingrecommendations and other crop management recommendations. The agronomicfactors may also be used to estimate one or more crop related results,such as agronomic yield. The agronomic yield of a crop is an estimate ofquantity of the crop that is produced, or in some examples the revenueor profit obtained from the produced crop.

In an embodiment, the agricultural intelligence computer system 130 mayuse a preconfigured agronomic model to calculate agronomic propertiesrelated to currently received location and crop information for one ormore fields. The preconfigured agronomic model is based upon previouslyprocessed field data, including but not limited to, identification data,harvest data, fertilizer data, and weather data. The preconfiguredagronomic model may have been cross validated to ensure accuracy of themodel. Cross validation may include comparison to ground truthing thatcompares predicted results with actual results on a field, such as acomparison of precipitation estimate with a rain gauge or sensorproviding weather data at the same or nearby location or an estimate ofnitrogen content with a soil sample measurement.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using field data provided by one or more data sources.FIG. 3 may serve as an algorithm or instructions for programming thefunctional elements of the agricultural intelligence computer system 130to perform the operations that are now described.

At block 305, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic data preprocessing offield data received from one or more data sources. The field datareceived from one or more data sources may be preprocessed for thepurpose of removing noise, distorting effects, and confounding factorswithin the agronomic data including measured outliers that couldadversely affect received field data values. Embodiments of agronomicdata preprocessing may include, but are not limited to, removing datavalues commonly associated with outlier data values, specific measureddata points that are known to unnecessarily skew other data values, datasmoothing, aggregation, or sampling techniques used to remove or reduceadditive or multiplicative effects from noise, and other filtering ordata derivation techniques used to provide clear distinctions betweenpositive and negative data inputs.

At block 310, the agricultural intelligence computer system 130 isconfigured or programmed to perform data subset selection using thepreprocessed field data in order to identify datasets useful for initialagronomic model generation. The agricultural intelligence computersystem 130 may implement data subset selection techniques including, butnot limited to, a genetic algorithm method, an all subset models'method, a sequential search method, a stepwise regression method, aparticle swarm optimization method, and an ant colony optimizationmethod. For example, a genetic algorithm selection technique uses anadaptive heuristic search algorithm, based on evolutionary principles ofnatural selection and genetics, to determine and evaluate datasetswithin the preprocessed agronomic data.

At block 315, the agricultural intelligence computer system 130 isconfigured or programmed to implement field dataset evaluation. In anembodiment, a specific field dataset is evaluated by creating anagronomic model and using specific quality thresholds for the createdagronomic model. Agronomic models may be compared and/or validated usingone or more comparison techniques, such as, but not limited to, rootmean square error with leave-one-out cross validation (RMSECV), meanabsolute error, and mean percentage error. For example, RMSECV can crossvalidate agronomic models by comparing predicted agronomic propertyvalues created by the agronomic model against historical agronomicproperty values collected and analyzed. In an embodiment, the agronomicdataset evaluation logic is used as a feedback loop where agronomicdatasets that do not meet configured quality thresholds are used duringfuture data subset selection steps (block 310).

At block 320, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic model creation basedupon the cross validated agronomic datasets. In an embodiment, agronomicmodel creation may implement multivariate regression techniques tocreate preconfigured agronomic data models.

At block 325, the agricultural intelligence computer system 130 isconfigured or programmed to store the preconfigured agronomic datamodels for future field data evaluation.

2.5. Hybrid Seed Classification Subsystem

In an embodiment, the agricultural intelligence computer system 130,among other components, includes the hybrid seed classificationsubsystem 170. The hybrid seed classification subsystem 170 isconfigured to generate a target success yield group of hybrid seedsspecifically identified for optimal performance on target fields. Asused herein the term “optimal” and related terms (e.g., “optimizing”,“optimization”, etc.) are broad terms that refer to the “best or mosteffective” with respect to any outcome, system, data etc. (“universaloptimization”) as well as improvements that are “better or moreeffective (“relative optimization”). The target success yield groupincludes a subset of one or more hybrid seeds, an estimated yieldforecast for each hybrid seed, and a probability of success of exceedingthe average estimated yield forecast for similarly classified hybridseeds.

In an embodiment, identifying hybrid seeds that will optimally performon target fields is based on input received by the agriculturalintelligence computer system 130 including, but not limited to,agricultural data records for multiple different hybrid seeds andgeo-location data related to the fields where the agricultural datarecords were collected. For example, if agricultural data records arereceived for one-hundred hybrid seeds, then the agricultural datarecords would include growth and yield data for the one-hundred hybridseeds and geo-location data about the fields where the one-hundredhybrid seeds were planted. In an embodiment, the agriculturalintelligence computer system 130 also receives geo-location andagricultural data for a second set of fields. The second set of fieldsare the target fields where the grower intends to plant selected hybridseeds. Information about the target fields are particularly relevant formatching specific hybrid seeds to the environment of the target fields.

The hybrid seed normalization instructions 172 provide instructions togenerate a dataset of hybrid seed properties that describerepresentative yield values and environmental classifications thatpreferred environmental conditions for each of the hybrid seeds receivedby the agricultural intelligence computer system 130. The probability ofsuccess generation instructions 174 provide instructions to generate adataset of success probability scores associated with each of the hybridseeds. The success probability scores describe the probability of asuccessful yield on the target fields. The yield classificationinstructions 176 provide instructions to generate a target success yieldgroup of hybrid seeds that have been identified for optimal performanceon target fields based on the success probability scores associated witheach of the hybrid seeds.

In an embodiment, the agricultural intelligence computer system 130 isconfigured to present, via the presentation layer 134, the targetsuccess yield group of selected hybrid seeds and their normalized yieldvalues and success probability scores.

Hybrid seed classification subsystem 170 and related instructions areadditionally described elsewhere herein.

2.6. Hybrid Seed Recommendation Subsystem

In an embodiment, the agricultural intelligence computer system 130,among other components, includes the hybrid seed recommendationsubsystem 180. The hybrid seed recommendation subsystem 180 isconfigured to generate a set of target hybrid seeds specificallyselected for optimal performance on target fields with minimized risk.The set of target hybrid seeds includes a subset of one or more hybridseeds that have estimated yield forecasts above a specific yieldthreshold and have an associated risk value that is below a specificrisk target.

In an embodiment, identifying a set of target hybrid seeds that willoptimally perform on target fields is based on an input set of hybridseeds that have been identified as having a specific probability ofproducing a successful yield on the target fields. The agriculturalintelligence computer system 130 may be configured to receive a set ofhybrid seeds as part of a target success yield group generated by thehybrid seed classification subsystem 170. The target success yield groupmay also include agricultural data specifying the probability of successfor each hybrid seed and other agricultural data such as yield value,relative maturity, and environmental observations from previouslyobserved harvests. In an embodiment, the agricultural intelligencecomputer system 130 also receives geo-location and agricultural data fora set of target fields. The “target fields” are fields where the groweris considering or intends to plant target hybrid seeds.

The hybrid seed filtering instructions 182 provide instructions tofilter and identify a subset of hybrid seeds that have a probability ofsuccess value that is above a specified success yield threshold. Therisk generation instructions 184 provide instructions to generate adataset of risk values associated with each of the hybrid seeds. Therisk values describe the amount of risk associated with each hybrid seedwith respect to the estimated yield value for each hybrid seed. Theoptimization classification instructions 186 provide instructions togenerate a dataset of target hybrid seeds that have average yield valuesabove a target threshold for a range of risk values from the dataset ofrisk values.

In an embodiment, the agricultural intelligence computer system 130 isconfigured to present, via the presentation layer 134, the set of targethybrid seeds and including their average yield values.

Hybrid seed recommendation subsystem 180 and related instructions areadditionally described elsewhere herein.

2.7. Implementation Example-Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 4 is a block diagram that illustrates a computersystem 400 upon which an embodiment of the invention may be implemented.Computer system 400 includes a bus 402 or other communication mechanismfor communicating information, and a hardware processor 404 coupled withbus 402 for processing information. Hardware processor 404 may be, forexample, a general purpose microprocessor.

Computer system 400 also includes a main memory 406, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 402for storing information and instructions to be executed by processor404. Main memory 406 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 404. Such instructions, when stored innon-transitory storage media accessible to processor 404, rendercomputer system 400 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 400 further includes a read only memory (ROM) 408 orother static storage device coupled to bus 402 for storing staticinformation and instructions for processor 404. A storage device 410,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 402 for storing information and instructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 414, including alphanumeric and other keys, is coupledto bus 402 for communicating information and command selections toprocessor 404. Another type of user input device is cursor control 416,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 404 and forcontrolling cursor movement on display 412. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 400 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 400 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 400 in response to processor 404 executing one or more sequencesof one or more instructions contained in main memory 406. Suchinstructions may be read into main memory 406 from another storagemedium, such as storage device 410. Execution of the sequences ofinstructions contained in main memory 406 causes processor 404 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 410. Volatile media includes dynamic memory, such asmain memory 406. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 402. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infrared data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 404 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infrared signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 418 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 418sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 428. Local network 422 and Internet 428 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 420and through communication interface 418, which carry the digital data toand from computer system 400, are example forms of transmission media.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution.

3. Functional Overview—Generate and Display Target Success Yield Groupof Hybrid Seeds

FIG. 7 depicts a detailed example of generating a target success yieldgroup of hybrid seeds identified for optimal yield performance on targetfields based on agricultural data records of the hybrid seeds andgeo-location data associated with the target fields.

3.1. Data Input

At step 705, the agricultural intelligence computer system 130 receivesagricultural data records from one or more fields for multiple differenthybrid seeds. In an embodiment, the agricultural data records mayinclude crop seed data for one or more hybrid seeds. Crop seed data caninclude historical agricultural data related to the planting, growing,and harvesting of specific hybrid seeds on one or more fields. Examplesof crop seed data may include, but are not limited to, historical yieldvalues, harvest time information, and relative maturity of a hybridseed, and any other observation data about the plant life cycle. Forexample, the agricultural data records may include hybrid seed data fortwo hundred (or more) different types of available corn hybrids. Thecrop seed data associated with each of the corn hybrids would includehistorical yield values associated with observed harvests, harvest timeinformation relative to planting, and observed relative maturity foreach of the corn hybrids on each of the observed fields. For instance,corn hybrid-001 may have agricultural data records that includehistorical yield data collected from twenty (or more) different fieldsover the past ten (or more) years.

In an embodiment, the agricultural data records may include fieldspecific data related to the fields where the crop seed data wasobserved. For example, field specific data may include, but is notlimited to, geo-location information, observed relative maturity basedon field geo-location, historical weather index data, observed soilproperties, observed soil moisture and water levels, and any otherenvironmental observations that may be specific to the fields wherehistorical crop seed data is collected. Field specific data may be usedto further quantify and classify crop seed data as it relates to each ofthe hybrid seeds. For example, different fields in differentgeo-locations may be better suited for different hybrid seeds based onrelative maturity of the hybrid seeds and the length of the growingseason. Fields within specific regions and sub-regions may have anassigned relative maturity for the growing season that is based on theclimate associated with the specific geo-location and the amount ofgrowing degree days (GDDs) available during the growing season.

FIG. 8 depicts an example of different regions within a state that havedifferent assigned relative maturity based on the growing seasondurations. State 805 is the state of Illinois and is divided intomultiple different regions and sub-regions. Examples of sub-regions mayinclude areas based on county, city, or town boundaries. Each of regions810, 815, 820, 825, and 830 represent geo-location specific regions thathave different growing season durations. For example, region 810represents a region of fields that based upon their geo-locations andthe associated climate have a shorter growing season because of coolerclimates. As a result, region 810 may be classified as fields that aresuited for hybrid seeds with a relative maturity of 100 days (shown as alegend of shades and respective GDD in FIG. 8 ). Region 815 is locatedsouth of region 100 and as a result may have warmer overall climates.Fields in region 815 may be classified as fields suited for hybrid seedswith a relative maturity of 105 days. Similarly, regions 820, 825, and830 are located further south than regions 810 and 815, and as a resultare classified with relative maturity classifications of 110, 115, and120 days respectively. Relative maturity classifications for differentregions may be used with historical yield data for hybrid seeds toassess how well hybrid seeds perform on fields based on rated relativematurities.

In an embodiment, specific field data within the agricultural datarecords may also include crop rotation data. Soil nutrient managementfor fields may depend on factors such as establishing diverse croprotations and managing the amount of tillage of the soil. For example,some historical observations have shown that a “rotation effect” ofrotating between different crops on a field may increase crop yield by 5to 15% over planting the same crop year over year. As a result, croprotation data within the agricultural data records may be used to helpdetermine a more accurate yield estimation.

In an embodiment, specific field data may include tillage data andmanagement practices used during the crop season. Tillage data andmanagement practices refer to the manner and schedule of tillageperformed on a particular field. Soil quality and the amount of usefulnutrients in the soil varies based upon the amount of topsoil. Soilerosion refers to the removal of topsoil, which is the richest layer ofsoil in both organic matter and nutrient value. One such practice thatcauses soil erosion is tillage. Tillage breaks down soil aggregates andincreases soil aeration, which may accelerate organic matterdecomposition. Therefore, tracking tillage management practices mayaccount for understanding the amount of soil erosion that occurs whichmay affect the overall yield of planted crop.

In an embodiment, the agricultural data records include historical cropseed data and field specific data from a set of test fields used todetermine hybrid seed properties by manufacturers. For example, MonsantoCorporation produces several commercial hybrid seeds and tests theircrop growth on multiple test fields. Monsanto Corp.'s test fields mayserve as an example of a set of test fields where agricultural datarecords are collected and received by the agricultural intelligencecomputer system 130. In another embodiment, the agricultural datarecords may include historical crop seed data and field specific datafrom sets of fields owned and operated by individual growers. These setsof fields where agricultural data records are collected may also be thesame fields designated as target fields for planting newly selectedcrops. In yet other embodiments, sets of fields owned and operated by agrower may provide agricultural data records used by other growers whendetermining the target success yield group of hybrid seeds.

Referring back to FIG. 7 , at step 710, the agricultural intelligencecomputer system 130 receives geo-location information for one or moretarget fields. Target fields represent the fields where the grower isconsidering planting or planning to plant the set of hybrid seedsselected from the target success yield group. In an embodiment, thegeo-location information for the one or more target fields may be usedin conjunction with the agricultural data records of specific fields todetermine which hybrid seeds, based on relative maturity and climate arebest suited for the target fields.

3.2. Agricultural Data Processing

At step 715, the hybrid seed normalization instructions 172 provideinstruction to generate a dataset of hybrid seed properties thatdescribe representative yield values and environmental classificationsfor each hybrid seed received as part of the agricultural data records.In an embodiment, the agricultural data records associated with hybridseeds are used to calculate a representative yield value and anenvironmental classification for each of the hybrid seeds. Therepresentative yield value is an expected yield value for a specifichybrid seed if planted in a field based on the historical yield valuesand other agricultural data observed from past harvests.

In an embodiment, the normalized yield value may be calculated bynormalizing multiple different yield observations from different fieldsacross different observed growth years. For example, fields where aspecific hybrid seed was first planted may be used to calculate anaverage first-year growth cycle yield for a specific hybrid seed. Theaverage first-year growth cycle yield for the specific hybrid seed mayinclude combining observed yield values from different fields overdifferent years. For instance, the specific hybrid seed may have beenplanted on fields tested during the product stage of Monsanto'scommercial product cycle (PS3, PS4, MD1, and MD2) over a time span of2009 through 2015. However, the first cycle of the specific hybrid seedmay have been planted on each of the fields on different years. Thefollowing table illustrates one such example:

2009 2010 2011 2012 2013 2014 2015 Cycle 1 PS3 PS4 MD1 MD2 Cycle 2 PS3PS4 MD1 MD2 Cycle 3 PS3 PS4 MD1 MD2 Cycle 4 PS3 PS4 MD1 MD2The columns of the table represent harvest years and the rows of thetable represent Monsanto commercial product development cycles, wherecycle 1 represents the 4 years of the hybrid seeds was planted onvarious fields and cycle 2 represents the second cycle of 4 years foranother set of hybrid seeds planted on the same field environments andso on.

In an embodiment, calculating normalized yield values may be based onsimilar cycles for the hybrid seed planted at the multiple fields. Forinstance, the normalized yield value for cycle 1 may be calculated as anaverage of the yield values observed on fields PS3 (2009), PS4 (2010),MD1 (2011), and MD2 (2012). By doing so, yield values may be averagedbased upon the common feature of how many growth cycles have occurred onthe particular fields. In other embodiments, calculating normalizedyield values may be based on other agricultural properties from theagricultural data records such as same year or same region/field.

In an embodiment, the environmental classification for each of thehybrid seeds may be calculated using a relative maturity field propertyassociated agricultural data records of the hybrid seeds. For example,the specific hybrid seed may have been planted across several fieldswithin region 820. Each of the fields within region 820 are classifiedas having an observed growth season that aligns with the relativematurity of 110 days. Therefore, based the fields associated with thespecific hybrid seed, the environmental classification for the specifichybrid seed may be assigned a relative maturity that equals that of theregion 820, which is 110 days. In other embodiments, if the fieldsassociated with historical observations of the specific hybrid seedcontain fields classified within multiple regions then the environmentalclassification may be calculated as an average of the different assignedrelative maturity values.

In an embodiment, the dataset of hybrid seed properties containsnormalized yield values for each hybrid seed and an environmentalclassification that describes the relative maturity value associatedwith the normalized yield value. In other embodiments, the dataset ofhybrid seed properties may also include properties related to the hybridseed growth cycle and field properties such as crop rotations, tillage,weather observations, soil composition, and any other agriculturalobservations.

Referring back to FIG. 7 , at step 720 the probability of successgeneration instructions 174 provide instruction to generate a dataset ofsuccess probability scores for each of the hybrid seeds which, describea probability of a successful yield as a probabilistic value ofachieving a successful yield relative to average yields of other hybridseeds with the same relative maturity. In an embodiment, the successprobability scores for the hybrid seeds are based upon the dataset ofhybrid seed properties with respect to the geo-locations associated withthe target fields. For example, relative maturity values associated withthe geo-locations of the target fields are used in part to determine theset of hybrid seeds to evaluate against in order to calculate a successprobability score for a particular hybrid seed. For instance, cornhybrid-002 may be a hybrid seed with a normalized yield calculated as7.5 bushels per acre and an assigned relative maturity of 100 GDD. Cornhybrid-002 is then compared against other hybrid seeds that have similarrelative maturity in order to determine whether corn hybrid-002 a goodcandidate for planting based upon the normalized yield value of cornhybrid-002 and the other hybrid seeds.

Machine learning techniques are implemented to determine probability ofsuccess scores for the hybrid seeds at the geo-locations associated withthe target fields. In an embodiment, the normalized yield values andassigned relative maturity values are used as predictor variables formachine learning models. In other embodiments, additional hybrid seedproperties such as, crop rotations, tillage, weather observations, soilcomposition, may also be used as additional predictor variables for themachine learning models. The target variable of the machine learningmodels is a probabilistic value ranging from 0 to 1, where 0 equals a 0%probability of a successful yield and 1 equals a 100% probability of asuccessful yield. In other embodiments, the target variable may be aprobabilistic value that may be scaled from 0 to 10, 1 to 10, or anyother scale of measurement. A successful yield is described as thelikelihood that the yield of a specific hybrid seed is a certain valueabove the mean yield for similarly classified hybrid seeds. For example,a successful yield may be defined as a yield that is 5 bushels per acreabove the mean yield of hybrid seeds that have the same assignedrelative maturity value.

FIG. 9 depicts a sample graph describing the range of normalized yieldvalues for hybrid seeds within a classified relative maturity. Meanvalue 905 represents the calculated mean yield value for hybrid seedsthat have the same relative maturity, such as 110 GDD. In an embodiment,determining which hybrid seeds have a significant normalized yield abovethe mean value 905 may be calculated by implementing a least significantdifference calculation. The least significant difference is a value at aparticular level of statistical probability. If the value is exceeded bythe difference between two means, then the two means are said to bedistinct. For example, if the difference between yield values of ahybrid seed and the calculated mean yield exceeds the least significantdifference value, then the yield for the hybrid seed is seen asdistinct. In other embodiments, determining significant differencesbetween yield values and the mean value 905 may be determined using anyother statistical algorithm.

Range 910 represents a range of yield values that are considered withinthe least significant difference value, and therefore are notsignificantly distinct. Threshold 915 represents the upper limit of therange 910. Normalized yield values above threshold 915 are thenconsidered to be significantly distinct from the mean value 905. In anembodiment, range 910 and threshold 915 may be configured to represent athreshold for determining which hybrid seed yields are considered to besignificantly higher than the mean value 905 and therefore a successfulyield value. For example, threshold 915 may be configured to equal avalue that is 5 bushels per acre above the mean value 905. In anembodiment, threshold 915 may be configured as a yield value that isdependent on the mean value 905, range 910, and the overall range ofyield values for the specific hybrid seeds that have the same relativematurity.

Range 920 represents a range of yield values for hybrid seeds that areconsidered successful yields. Hybrid seed 925 represents a specifichybrid seed within the range 920 that has a normalized yield value abovethe threshold 915. In an embodiment, machine learning models may beconfigured to use the range 910 and threshold 915 when calculatingprobability of success scores between 0 and 1. Different machinelearning models may include, but are not limited to, logisticregression, random forest, vector machine modelling, and gradient boostmodelling.

In an embodiment, logistic regression may be implemented as the machinelearning technique to determine probability of success scores for eachof the hybrid seeds for the target fields. For logistic regression, theinput values for each hybrid seed are the normalized yield value and theenvironmental classification, which is specified as relative maturity.The functional form of the logistic regression is:

${{P\left( {y = {1{❘{{x_{1} = \underline{{yld}_{i}}},{x_{2} = \underline{{RM}_{j}}}}}}} \right)} = \frac{e^{a + {b*x_{1}} + {c*x_{2}}}}{1 + e^{a + {b*x_{1}} + {c*x_{2}}}}},$

where P(y=1|x₁=yld_(i) , x₂=RM_(j) ) is the probability of success (y=1)for product i with normalized yield value and in target field j with agiven relative maturity; constants a, b and c are the regressioncoefficients estimated through historical data. The output of thelogistic regression is a set of probability scores between 0 and 1 foreach hybrid seed specifying success at the target field based upon therelative maturity assigned to the geo-location associated with thetarget fields.

In another embodiment, a random forest algorithm may be implemented asthe machine learning technique to determine probability of successscores for each of the hybrid seeds for the target fields. Random forestalgorithm is an ensemble machine learning method that operates byconstructing multiple decision trees during a training period and thenoutputs the class that is the mean regression of the individual trees.The input values for each hybrid seed are the normalized yield value andthe environmental classification as relative maturity. The output is aset of probability scores for each hybrid seed between 0 and 1.

In another embodiment, support vector machine (SVM) modelling may beimplemented as the machine learning technique to determine probabilityof success scores for each of the hybrid seeds for the target fields.Support vector machine modelling is a supervised learning model used toclassify whether input using classification and regression analysis.Input values for the support vector machine model are the normalizedyield values and the environmental classification relative maturityvalues for each hybrid seed. The output is a set of probability scoresfor each hybrid seed between 0 and 1. In yet another embodiment,gradient boost (GBM) modelling may be implemented as the machinelearning technique, where the input values are the normalized yieldvalues and the environmental classification relative maturity values foreach hybrid seed. Gradient boost is a technique for regression andclassification problems, which produces a prediction model in the formof an ensemble of weak prediction models, such as decision trees.

Referring to FIG. 7 , at step 725 the yield classification instructions176 generate a target success yield group made up of a subset of thehybrid seeds that have been identified as having a high probability toproduce a yield that is significantly higher than the average yield forother hybrid seeds within the same relative maturity classification forthe target fields. In an embodiment, the target success yield groupcontains hybrid seeds that have probability of success values that areabove a specific success probability threshold. The success probabilitythreshold may be configured probability value that is associated withyields that are significantly higher than the mean yield of other hybridseeds. For example, if at step 720 the yield threshold for successfulyields is equal to five bushels per acre above the mean value, then thesuccess probability threshold may be associated with a probability ofsuccess value equal to that of the yield threshold. For instance, if theyield threshold equals five bushels per acre above the mean yield andhas a probability of success value as 0.80 then the success probabilitythreshold may be assigned 0.80. In this example, the target successyield group would contain hybrid seeds that have probability of successvalues equal to or greater than 0.80.

In other embodiments, the success probability threshold may beconfigured to be higher or lower depending on whether the grower desiresa smaller or larger target success yield group respectively.

3.3. Present Target Success Yield Group

In an embodiment, the target success yield group contains hybrid seedsthat have an assigned relative maturity value that equals the relativematurity associated with the target fields. At step 730, thepresentation layer 134 of the agricultural intelligence computer system130 is configured to display or cause display, on a display device onthe field manager computing device 104, of the target success yieldgroup and normalized yield values for each hybrid seed within the targetsuccess yield group. In another embodiment, the presentation layer 134may communicate the display of the target success yield group to anyother display devices that may be communicatively coupled to theagricultural intelligence computer system 130, such as remote computerdevices, display devices within a cab, or any other connected mobiledevices. In yet another embodiment, the presentation layer 134 maycommunicate the target success yield group to other systems andsubsystems with the agricultural intelligence computer system 130 forfurther processing and presentation.

In an embodiment, the presentation layer 134 may display additionalhybrid seed property data and other agricultural data that may berelevant to the grower. The presentation layer 134 may also sort thehybrid seed in the target success yield group based on the probabilityof success values. For example, the display of hybrid seeds may besorted in descending order of probability of success values such thatthe grower is able to view the most successful hybrid seeds for histarget fields first.

In some embodiments, the after receiving the information displayed, agrower may act on the information and plant the suggested hybrid seeds.In some embodiments, the growers may operate as part of the organizationthat is determining the target success yield group, and/or may beseparate. For example, the growers may be clients of the organizationdetermining the target success yield group and may plant seed based onthe target success yield group.

4. Functional Overview—Generating and Displaying Target Hybrid Seeds forPlanting

FIG. 10 depicts a detailed example of generating a set of target hybridseeds identified for optimal yield performance and managed risk ontarget fields based on agricultural data records of the hybrid seeds andgeo-location data associated with the target fields.

4.1. Data Input

At step 1005, the agricultural intelligence computer system 130 receivesa dataset of candidate hybrid seeds including one or more hybrid seedssuited for planting on target fields, probability of success valuesassociated with each hybrid seed, and historical agricultural dataassociated with each hybrid seed. In an embodiment, the dataset ofcandidate hybrid seeds may include a set of one or more hybrid seedsidentified by the hybrid seed classification subsystem 170 as having ahigh probability to produce successful yield values on the target fieldsand historical agricultural data associated with each hybrid seed in theset of candidate hybrid seeds. The target success yield group generatedat step 725 in FIG. 7 may represent the dataset of candidate hybridseeds.

In an embodiment, the historical agricultural data may includeagricultural data related to the planting, growing, and harvesting ofspecific hybrid seeds on one or more fields. Examples of agriculturaldata may include, but are not limited to, historical yield values,harvest time information, and relative maturity of a hybrid seed, andany other observation data about the plant lifecycle. For example, ifthe dataset of candidate hybrid seeds is the target success yield groupfrom the hybrid seed classification subsystem 170, then the agriculturaldata may include an average yield value and a relative maturity assignedto each hybrid seed.

At step 1010, the agricultural intelligence computer system 130 receivesdata about the target fields where the grower is planning to plant theset of target hybrid seeds. In an embodiment, the data about the targetfields is property information that includes, but is not limited to,geo-location information for the target fields and dimension and sizeinformation for each of the target fields. In an embodiment, thegeo-location information for the target fields may be used inconjunction with the historical agricultural data to determine optimalset of target hybrid seeds and amount of each of the target hybrid seedsto plant on each of the target fields based on relative maturity andclimate of the target fields.

4.2. Hybrid Seed Selection

At step 1015, the hybrid seed filtering instructions 182 provideinstruction to select a subset of one or more hybrid seeds from thecandidate set of hybrid seeds that have a probability of success valuegreater than or equal to a target probability filtering threshold. In anembodiment, the target probability filtering threshold is a configuredthreshold of the probability of success value associated with each ofthe hybrid seeds in the candidate set of hybrid seeds. The targetprobability filtering threshold may be used to further narrow theselection pool of hybrid seeds based upon only selecting the hybridseeds that have a certain probability of success. In an embodiment, ifthe candidate set of hybrid seeds represents the target success yieldgroup generated at step 725, then it is likely that the set of hybridseeds have already been filtered to only include hybrid seeds with ahigh probability of success value. In one example, the targetprobability filtering threshold may have the same threshold value as thesuccessful yield threshold used to generate the target success yieldgroup. If that is the case, then the subset of one or more hybrid seedsmay include the entire set of hybrid seeds. In another example, thegrower may desire a more narrowed list of hybrid seeds, which may beachieved by configuring a higher probability of success value for thetarget probability filtering threshold to filter out the hybrid seedsthat have lower than desired probability of success values.

At step 1020, the seed normalization instructions 172 provideinstruction to generate a representative yield value for each hybridseed in the subset of one or more hybrid seeds based on yield valuesfrom the historical agricultural data for each of the hybrid seeds. Inan embodiment, representative yield value is an expected yield value fora specific hybrid seed if planted in a field based on the historicalyield values and other agricultural data observed from past harvests. Inan embodiment, the representative yield value is a calculated average ofyields from multiple different observed growth seasons on multiplefields. For example, the representative yield value may be calculated asan average of different observed growth cycle years, where an averagefirst-year growth cycle yield for the specific hybrid seed mayincorporate combining observed yield values from different fields overdifferent years. After calculating average growth cycle yields fordifferent growth cycle years, each of the averages may be combined togenerate a representative average yield for each specific hybrid seed.In another embodiment, the representative yield value may be thenormalized yield value calculated at step 715.

4.3. Generate Risk Values for Hybrid Seeds

At step 1025, the risk generation instructions 184 provide instructionto generate a dataset of risk values for each hybrid seed in the subsetof one or more hybrid seeds based upon historical agricultural dataassociated with each of the hybrid seeds. Risk values describe theamount of risk, in terms of yield variability, for each hybrid seedbased upon the representative yield value. For example, if for cornhybrid-002 the representative yield is fifteen bushels per acre however,the variability for corn hybrid-002 is high such that the yield mayrange from five bushels per acre to twenty-five bushels per acre, thenit is likely that the representative yield for corn hybrid-002 is not agood representation of actual yield because the yield may vary betweenfive and twenty-five bushels per acre. High risk values are associatedwith high variability on yield return, whereas low risk values areassociated with low variability on yield return and yield outcomes thatare more closely aligned to the representative yield.

In an embodiment, risk values for hybrid seeds are based on thevariability between year-to-year yield returns for a specific hybridseed over two or more years. For example, calculating a risk value forcorn hybrid-002 includes calculating the variability of yield valuesfrom multiple years of yield output from the historical agriculturaldata. The variance in yield output from 2015 and 2016 for cornhybrid-002 may be used to determine a risk value that may be associatedwith the representative yield value for corn hybrid-002. Determining thevariance of yield output is not limited to using yield output from twoprevious years, variance may be calculated with yield output data frommultiple years. In an embodiment, the calculated risk values may berepresented in terms of a standard deviation of bushel per acre, wherestandard deviation is calculated as the square root of the calculatedvariance of risk.

In an embodiment, risk values for hybrid seeds may be based on thevariability of yield output from field-to-field observations for aspecific year. For example, calculating a risk value associated withfield variability may include determining the variability of yields fromeach field observed for a specific hybrid seed for a specific year. Iffor a specific hybrid seed the observed yield output across multiplefields ranges from five to fifty bushels per acre, then the specifichybrid seed may have high field variability. As a result, the specifichybrid seed may be assigned a high-risk factor based on fieldvariability because expected output on any given field may vary betweenfive to fifty bushels per acre instead of being closer to therepresentative yield value.

In another embodiment, risk values for hybrid seeds may be based uponvariability between year-to-year yield returns and variability betweenfield-to-field observations. Both the year-to-year risk values and thefield-to-field risk values may be combined to represent a risk valuethat incorporates variability of yield output across multiple observedfields and multiple observed seasons. In yet other embodiments, riskvalues may incorporate other observed crop seed data associated withhistorical crop growth and yield.

4.4. Generate Dataset of Target Hybrid Seeds

At step 1030, the optimization classification instructions 186 provideinstruction to generate a dataset of target hybrid seeds for planting onthe target fields based on the dataset of risk values, therepresentative yield values for the hybrid seeds, and the one or moreproperties for the target fields. In an embodiment, the target hybridseeds in the dataset of target hybrid seeds are selected based upontheir representative yield values and the associated risk values fromthe dataset of risk values.

Determining which combination of hybrid seeds to include in the datasetof target hybrid seeds involves determining a relationship between therepresentative yield for a specific hybrid seed and the risk valueassociated with the specific hybrid seed. Choosing hybrid seeds thathave high representative yields may not result in an optimal set ofhybrid seeds if the high yield hybrid seeds also carry a high level ofrisk. Conversely, choosing hybrid seeds that have low risk values maynot have a high enough yield return on investment.

In an embodiment, the hybrid seeds from the subset of one or more hybridseeds may be graphed based on their respective representative yieldvalues versus their associated risk values. FIG. 11 depicts an examplegraph 1105 of yield versus risk for the subset of one or more hybridseeds. The y-axis 1110 represents the representative yield, as expectedyield, for the hybrid seeds and the x-axis 1115 represents the riskvalues for the hybrid seeds expressed as standard deviation. Byrepresenting risk values as standard deviation, the unit of the riskvalues may be the same as the units for representative yield, which isbushels per acre. Dots on graph 1105, represented by group 1125 andgroup 1130 represent each of the hybrid seeds from the subset of one ormore hybrid seeds. For example, graph 1105 shows that hybrid seed 1135has a representative yield value two hundred bushels per acre and a riskvalue having a standard deviation of one hundred ninety-one bushels peracre. In other embodiments, graph 1105 may be generated using differentunits such as profit per acre measured in dollars or any other derivedunit of measurement.

In an embodiment, determining which hybrid seeds belong in the datasetof target hybrid seeds involves determining an expected yield return fora specified amount of risk. To generate set of target hybrid seeds thatwill likely be resilient to various environmental and other factors, itis preferable to generate a diverse set of hybrid seeds that containshybrid seeds with both lower and higher risk values as well as moderateto high yield output. Referring to FIG. 10 , step 1032 representsgenerating a target threshold of representative yield values for a rangeof risk values. In an embodiment, the optimization classificationinstructions 186 provide instruction to calculate an optimal frontiercurve that represents a threshold of optimal yield output with amanageable amount of risk tolerance over the range of risk values. Afrontier curve is a fitted curve that represents the optimal output withrespect to the graphed input values considering optimal efficiency. Forexample, graph 1105 contains hybrid seeds based on representative yieldversus risk value, where it may be inferred that a specific hybrid seedthat has a higher yield is likely to also have higher risk. Conversely,hybrid seeds that have lower risk values are likely to have lowerrepresentative yield values. Frontier curve 1120 represents an optimalcurve that tracks the optimal amount of yield based on a range of riskvalues.

At step 1034, the optimization classification instructions 186 provideinstruction to select hybrid seeds that make up the set of target hybridseeds by selecting the hybrid seeds that have a representative yield andrisk value that meets the threshold defined by the frontier curve 1120.Hybrid seeds that fall on or near the frontier curve 1120 provide theoptimal level of yield at the desired level of risk. Target hybrid seeds1140 represent the optimal set of hybrid seeds for the dataset of targethybrid seeds. Hybrid seeds that fall under the frontier curve 1120 havesub-optimal yield output for the level of risk or have higher thandesired risk for the level of yield output produced. For example, hybridseed 1135 is under the frontier curve 1120 and may be interpreted ashaving lower than optimal yield for its amount of risk, as shown by theplacement of hybrid seed 1135 being vertically below the frontier curve1120. Also, hybrid seed 1135 may be interpreted as having higher thanexpected risk for its yield output, as shown by the placement of hybridseed 1135 being horizontally to the right of the frontier curve 1120 forthat amount of representative yield. Hybrid seeds 1135 that are not onor near the frontier curve 1120 have sub-optimal representative yieldfor their associated risk values and are therefore not included in theset of target hybrid seeds. Additionally, hybrid seeds 1135 representhybrid seeds that have a higher than desired risk value and aretherefore not included in the set of target hybrid seeds.

In an embodiment, the optimization classification instructions 186provide instruction to generate allocation instructions for each targethybrid seed in the set of target hybrid seeds. Allocation instructionsdescribe an allocation quantity of seeds for each target hybrid seed inthe set of target hybrid seeds that provide an optimal allocationstrategy to a grower based upon the amount and location of the targetfields. For example, allocation instructions for a set of target hybridseeds that includes seeds (CN-001, CN-002, SOY-005, CN-023) may includean allocation of 75% of CN-001, 10% of CN-002, 13% of SOY-005, and 2% ofCN-023. Embodiments of the allocation instructions may include, but arenot limited to, number of bags of seeds, a percentage of the total seedsto be planted across the target fields, or an allotment number of acresfor each target hybrid seed to be planted. In an embodiment, determiningallocation amounts may be calculated using a third-party optimizationsolver product, such as CPLEX Optimizer by IBM. The CPLEX Optimizer is amathematical programming solver for linear programming, mixed integerprogramming, and quadratic programming. Optimization solvers, such asCPLEX Optimizer, are configured to evaluate the representative yieldvalues and risk values associated with the target hybrid seeds anddetermine a set of allocation instructions for allocating amounts ofseeds for each of the target hybrid seeds in the set of target hybridseeds. In an embodiment, the optimization solver may use the sum of therepresentative yield values of target hybrid seeds and a calculated sumof risk values of the target hybrid seeds to calculate a configuredtotal risk threshold that may be used to determine the upper limits ofallowed risk and yield output for the set of target hybrid seeds.

In another embodiment, the optimization solver may also input targetfield data describing size, shape, and geo-location of each of thetarget fields, in order to determine allocation instructions thatinclude placement instructions for each of the allotments of targethybrid seeds. For example, if a particular target field is shaped orsized in a particular way, the optimization solver may determine thatallotment of one target hybrid seed is preferable on the particularfield as opposed to planting multiple target hybrid seeds on theparticular field. The optimization solver is not limited to the CPLEXOptimizer, other embodiments may implement other optimization solvers orother optimization algorithms to determine sets of allocationinstructions for the set of target hybrid seeds.

4.5. Seed Portfolio Analysis

Step 1030 described determining and generating the set of target hybridseeds for a grower based on the target fields using the frontier curveto determine the optimal yield output for the desired level of risks. Inan embodiment, the optimization classification instructions 186 provideinstruction to configure the frontier curve to determine overall optimalperformance for a grower's seed portfolio relative to other growerswithin the same region or sub-region. For example, representative yieldoutput and overall risk values may be calculated for each grower withina specific region. For example, using historical agricultural data formultiple growers, the representative yield values and associated riskvalues for hybrid seeds planted by each grower may be aggregated togenerate an aggregated yield output value and aggregated risk valueassociated with each grower. Then the aggregated values for each growermay be graphed on a seed portfolio graph, similar to graph 1105, wherethe individual dots on the graph may represent a grower's aggregatedhybrid seed yield output and aggregated risk. In an embodiment, thefrontier curve may be generated to determine an optimal aggregated yieldoutput and aggregated risk value for the growers in the specific region.Growers that are on or near the frontier curve may represent growerswhose seed portfolio produces the optimal amount of yield with a managedamount of risk. Growers that are below the frontier curve representgrowers that are not maximizing their output based on their risk.

In an embodiment, the optimization classification instructions 186provide instruction to generate an alert message for a particular growerif the aggregated yield output and aggregated risk for the grower's seedportfolio does not meet the optimal threshold for the seed portfolio asdescribed by the frontier curve on a seed portfolio graph. Thepresentation layer 134 may be configured to present and send the alertmessage to the field manager computing device 104 for the grower. Thegrower may then have the option of requesting a set of target hybridseeds that may provide optimal yield output for future growing seasons.

4.6. Present Set of Target Hybrid Seeds

In an embodiment, the dataset of target hybrid seeds may contain therepresentative yield values and risk values, from the dataset of riskvalues, associated with each target hybrid seed in the dataset of targethybrid seeds for the target fields. Referring to FIG. 10 , at step 1035the presentation layer 134 of the agricultural intelligence computersystem 130 is configured to communicate a display, on a display deviceon the field manager computing device 104, of the dataset of targethybrid seeds including the representative yield values and associatedrisk values for each target hybrid seed. In another embodiment, thepresentation layer 134 may communicate the display of the dataset oftarget hybrid seeds to any other display devices that may becommunicatively coupled to the agricultural intelligence computer system130, such as remote computer devices, display devices within a cab, orany other connected mobile devices. In yet another embodiment, thepresentation layer 134 may communicate the dataset of target hybridseeds to other systems and subsystems with the agricultural intelligencecomputer system 130 for further processing and presentation.

In an embodiment, the presentation layer 134 may display allocationinstructions, including seed allotments and placement information, foreach target hybrid seed. The presentation layer 134 may also sort thetarget hybrid seeds based on allotment quantity or may present thetarget hybrid seeds based on placement strategy on the target fields.For example, the display of target hybrid seeds and allocationinstructions may be superimposed onto a map of the target fields so thatthe grower may visualize planting strategy for the upcoming season.

In some embodiments, growers can take in the information presentedrelated to allocation instructions and plant seeds based on theallocation instructions. The growers may operate as part of theorganization that is determining the allocation instructions, and/or maybe separate. For example, the growers may be clients of the organizationdetermining the allocation instructions and may plant seed based on theallocation instructions.

What is claimed is:
 1. A computer-implemented method comprising:generating, by a server, representative yield values for one or moreseeds based on historical agricultural data; generating, by the server,a dataset of risk values for the one or more seeds, the dataset of riskvalues indicative of an amount of risk based on yield variabilityassociated with the representative yield values for the one or moreseeds based on the historical agricultural data; generating, by theserver, a dataset of target seeds, from the one or more seeds, forplanting in one or more target fields based on: the dataset of riskvalues for the one or more seeds, the representative yield values forthe one or more seeds, and one or more properties for the one or moretarget fields; generating, by the server, allocation instructions forthe target seeds included in the dataset of target seeds, the allocationinstructions indicative of, for each target seed in the dataset oftarget seeds, a planting quantity for the target seed and a plantinglocations for the target seed within the one or more target fields; andcausing display of, on a display device, the dataset of target seedsincluding: the representative yield value of each of the target seeds,the amount of risk for each of the target seeds, and an indication ofthe one or more target fields.
 2. The computer-implemented method ofclaim 1, further comprising, prior to generating the representativeyield values: determining a dataset representative of multiple candidateseeds, the dataset including probability of success values associatedwith each of the multiple candidate seeds, which describe a probabilityof a successful yield, and historical agricultural data associated witheach of the multiple candidate seeds; and selecting the one or moreseeds as a subset of the multiple candidate seeds, the one or more seedshaving probability of success values greater than a target probabilityof success filtering threshold.
 3. The computer-implemented method ofclaim 2, wherein the probability of success values indicate aprobability that a yield value of a respective one of the candidateseeds exceeds an average yield value of other ones of the candidateseeds based on historical agricultural data.
 4. The computer-implementedmethod of claim 1, wherein historical agricultural data comprises annualyield output as bushels per acre and seed relative maturity.
 5. Thecomputer-implemented method of claim 1, wherein the one or moreproperties for the one or more target fields comprise geo-location andsize of each target field in the one or more target fields.
 6. Thecomputer-implemented method of claim 1, wherein the allocationinstructions are generated based on a sum of the representative yieldvalues for the target seeds included in the dataset of target seeds anda calculated sum of risk values for the target seeds included in thedataset of target seeds that is below a configured total risk threshold.7. The computer-implemented method of claim 1, further comprisingcausing display of, on the display device, the allocation instructionsfor each target seed in the dataset of target seeds.
 8. Thecomputer-implemented method of claim 1, wherein generating the datasetof risk values for the one or more seeds includes calculating ayear-over-year variance risk of yield values for each seed of the one ormore seeds as a variance of yield values over two or more years for aspecific seed based on the historical agricultural data for the specificseed.
 9. The computer-implemented method of claim 1, wherein generatingthe dataset of risk values for the one or more seeds includescalculating a field-by-field variance risk of yield values for each seedof the one or more seeds as a variance of yield values from two or morefields for a specific seed for a specific year based on the historicalagricultural data for the specific seed.
 10. The computer-implementedmethod of claim 1, wherein generating the dataset of target seedsincludes: using an optimal frontier curve to generate a specific targetthreshold for the representative yield values for a range of the riskvalues included in the dataset of risk values; and selecting the targetseeds that have representative yield values that satisfy the specifictarget threshold for the range of risk values from the dataset of riskvalues.
 11. An agricultural intelligence computer system comprising: aprocessor and a non-transitory computer-readable storage medium, whichincludes instructions which, when executed using the processor, causethe processor to: generate representative yield values for one or moreseeds based on historical agricultural data; generate a dataset of riskvalues for the one or more seeds, the dataset of risk values indicativeof an amount of risk based on yield variability associated with therepresentative yield values for of the one or more seeds based on thehistorical agricultural data; generate a dataset of target seeds, fromthe one or more seeds, for planting in one or more target fields basedon: the dataset of risk values for the one or more seeds, therepresentative yield values for the one or more seeds, and one or moreproperties for the one or more target fields; generate allocationinstructions for the target seeds included in the dataset of targetseeds, the allocation instructions indicative of, for each target seedin the dataset of target seeds, a planting quantity for the target seedand a planting location for the target seed within the one or moretarget fields; and cause display of, on a display device, the dataset oftarget seeds including: the representative yield value of each of thetarget seeds, the amount of risk for each of the target seeds, and anindication of the one or more target fields.
 12. The agriculturalintelligence computer system of claim 11, wherein the instructions, whenexecuted using the processor, further cause the processor to, prior togenerating the representative yield values: determine a dataset ofmultiple candidate seeds, the dataset of multiple candidate seedsincluding probability of success values associated with each of themultiple candidate seeds, which describe a probability of a successfulyield, and historical agricultural data associated with each of themultiple candidate seeds; and select the one or more seeds as a subsetof the multiple candidate seeds, based on the one or more seeds havingprobability of success values greater than a target probability ofsuccess filtering threshold.
 13. The agricultural intelligence computersystem of claim 12, wherein the probability of success values indicate aprobability that a yield value of a respective one of the candidateseeds exceeds an average yield value of other ones of the candidateseeds based on historical agricultural data.
 14. The agriculturalintelligence computer system of claim 11, wherein historicalagricultural data comprises annual yield output as bushels per acre andseed relative maturity.
 15. The agricultural intelligence computersystem of claim 11, wherein the one or more properties for the one ormore target fields comprise geo-location and size of each target fieldin the one or more target fields.
 16. The agricultural intelligencecomputer system of claim 11, wherein the allocation instructions arebased on a sum of the representative yield values for the target seedsincluded in the dataset of target seeds and a calculated sum of riskvalues for the target seeds included in the dataset of target seeds thatis below a configured total risk threshold.
 17. The agriculturalintelligence computer system of claim 11, wherein the instructions, whenexecuted using the processor, further cause the processor to causedisplay of, on the display device, the allocation instructions for eachtarget seed in the dataset of target seeds.
 18. The agriculturalintelligence computer system of claim 11, wherein the instructions, whenexecuted using the processor, further cause the processor, in generatingthe dataset of risk values for the one or more seeds, to calculate ayear-over-year variance risk of yield values for each seed of the one ormore seeds as a variance of yield values over two or more years for aspecific seed based on the historical agricultural data for the specificseed.
 19. The agricultural intelligence computer system of claim 11,wherein the instructions, when executed using the processor, furthercause the processor, in generating the dataset of risk values for theone or more seeds, to calculate a field-by-field variance risk of yieldvalues for each seed of the one or more seeds as a variance of yieldvalues from two or more fields for a specific seed for a specific yearbased on the historical agricultural data for the specific seed.
 20. Theagricultural intelligence computer system of claim 11, wherein theinstructions, when executed using the processor, further cause theprocessor, in generating the dataset of target seeds, to: use an optimalfrontier curve to generate the specific target threshold for therepresentative yield values for the range of risk values; and select thetarget seeds that have representative yield values that meet thespecific target threshold for the range of risk values from the datasetof risk values.